



'''simple docstring'''


from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						import torch

						from transformers.modeling_outputs import BaseModelOutput

						from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowercase_   :					List[str]        =     logging.get_logger(__name__)




@add_end_docstrings(_UpperCAmelCase      )
class   __UpperCamelCase							(_UpperCAmelCase      ):







	def __init__(     self			,					**_lowerCAmelCase    )     ->     int:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase    )

							if self.framework == "tf":
													raise ValueError(F"""The {self.__class__} is only available in PyTorch."""    )

							requires_backends(self			,					"""vision"""    )
							self.check_model_type(_lowerCAmelCase    )







	def __call__(     self			,					_lowerCAmelCase			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     Any:
							'''simple docstring'''






							if "text_queries" in kwargs:
													lowercase   =				kwargs.pop("""text_queries"""    )

							if isinstance(_lowerCAmelCase			,					(str, Image.Image)    ):
													lowercase   =				{'image': image, 'candidate_labels': candidate_labels}
							else:
													lowercase   =				image
							lowercase   =				super().__call__(_lowerCAmelCase			,					**_lowerCAmelCase    )
							return results







	def 	_a (     self			,					**_lowerCAmelCase    )     ->     Optional[int]:
							'''simple docstring'''






							lowercase   =				{}
							if "threshold" in kwargs:
													lowercase   =				kwargs['threshold']
							if "top_k" in kwargs:
													lowercase   =				kwargs['top_k']
							return {}, {}, postprocess_params







	def 	_a (     self			,					_lowerCAmelCase    )     ->     str:
							'''simple docstring'''






							lowercase   =				load_image(inputs["""image"""]    )
							lowercase   =				inputs['candidate_labels']
							if isinstance(_lowerCAmelCase			,					_lowerCAmelCase    ):
													lowercase   =				candidate_labels.split(""","""    )

							lowercase   =				torch.tensor([[image.height, image.width]]			,					dtype=torch.intaa    )
							for i, candidate_label in enumerate(_lowerCAmelCase    ):
													lowercase   =				self.tokenizer(_lowerCAmelCase			,					return_tensors=self.framework    )
													lowercase   =				self.image_processor(_lowerCAmelCase			,					return_tensors=self.framework    )
													yield {
													    "is_last": i == len(_lowerCAmelCase    ) - 1,
													    "target_size": target_size,
													    "candidate_label": candidate_label,
													    **text_inputs,
													    **image_features,
													}







	def 	_a (     self			,					_lowerCAmelCase    )     ->     List[Any]:
							'''simple docstring'''






							lowercase   =				model_inputs.pop("""target_size"""    )
							lowercase   =				model_inputs.pop("""candidate_label"""    )
							lowercase   =				model_inputs.pop("""is_last"""    )

							lowercase   =				self.model(**_lowerCAmelCase    )

							lowercase   =				{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
							return model_outputs







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase=0.1			,					_lowerCAmelCase=None    )     ->     str:
							'''simple docstring'''






							lowercase   =				[]
							for model_output in model_outputs:
													lowercase   =				model_output['candidate_label']
													lowercase   =				BaseModelOutput(_lowerCAmelCase    )
													lowercase   =				self.image_processor.post_process_object_detection(
													    outputs=_lowerCAmelCase			,					threshold=_lowerCAmelCase			,					target_sizes=model_output["""target_size"""]    )[0]

													for index in outputs["scores"].nonzero():
																			lowercase   =				outputs['scores'][index].item()
																			lowercase   =				self._get_bounding_box(outputs["""boxes"""][index][0]    )

																			lowercase   =				{'score': score, 'label': label, 'box': box}
																			results.append(_lowerCAmelCase    )

							lowercase   =				sorted(_lowerCAmelCase			,					key=lambda _lowerCAmelCase    : x["score"]			,					reverse=_lowerCAmelCase    )
							if top_k:
													lowercase   =				results[:top_k]

							return results







	def 	_a (     self			,					_lowerCAmelCase    )     ->     Dict[str, int]:
							'''simple docstring'''






							if self.framework != "pt":
													raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""    )
							lowercase   =				box.int().tolist()
							lowercase   =				{
							    'xmin': xmin,
							    'ymin': ymin,
							    'xmax': xmax,
							    'ymax': ymax,
							}
							return bbox










"""simple docstring"""


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			len(a__ )
						print('The following activities are selected:' )

						# The first activity is always selected
						lowerCAmelCase   :Dict						=			0
						print(a__     ,      end=',' )

						# Consider rest of the activities
						for j in range(a__ ):
												# If this activity has start time greater than
												# or equal to the finish time of previously
												# selected activity, then select it
												if start[j] >= finish[i]:
																		print(a__     ,      end=',' )
																		lowerCAmelCase   :List[str]						=			j


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__SCREAMING_SNAKE_CASE   =   [1, 3, 0, 5, 8, 5]
		__SCREAMING_SNAKE_CASE   =   [2, 4, 6, 7, 9, 9]
		print_max_activities(start, finish)